{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<!-- This should be added to the overrides/main.html and improved-->\n",
    "<div class=\"grid cards\" markdown>\n",
    "\n",
    "- <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"50\" width=\"50\" viewBox=\"0 0 488 512\"><!--!Font Awesome Free 6.6.0 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free Copyright 2024 Fonticons, Inc.--><path fill=\"#2094F3\" d=\"M488 261.8C488 403.3 391.1 504 248 504 110.8 504 0 393.2 0 256S110.8 8 248 8c66.8 0 123 24.5 166.3 64.9l-67.5 64.9C258.5 52.6 94.3 116.6 94.3 256c0 86.5 69.1 156.6 153.7 156.6 98.2 0 135-70.4 140.8-106.9H248v-85.3h236.1c2.3 12.7 3.9 24.9 3.9 41.4z\"/></svg>\n",
    "<a href=\"https://colab.research.google.com/github/AmbiqAI/heartkit/blob/main/docs/guides/ecg-foundation-model.ipynb\" class=\"md-content__button md-icon\" style=\"color: #2094F3;\">\n",
    "    View in Colab\n",
    "</a>\n",
    "\n",
    "- <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"50\" width=\"50\" viewBox=\"0 0 496 512\"><!--!Font Awesome Free 6.6.0 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free Copyright 2024 Fonticons, Inc.--><path fill=\"#2094F3\" d=\"M165.9 397.4c0 2-2.3 3.6-5.2 3.6-3.3 .3-5.6-1.3-5.6-3.6 0-2 2.3-3.6 5.2-3.6 3-.3 5.6 1.3 5.6 3.6zm-31.1-4.5c-.7 2 1.3 4.3 4.3 4.9 2.6 1 5.6 0 6.2-2s-1.3-4.3-4.3-5.2c-2.6-.7-5.5 .3-6.2 2.3zm44.2-1.7c-2.9 .7-4.9 2.6-4.6 4.9 .3 2 2.9 3.3 5.9 2.6 2.9-.7 4.9-2.6 4.6-4.6-.3-1.9-3-3.2-5.9-2.9zM244.8 8C106.1 8 0 113.3 0 252c0 110.9 69.8 205.8 169.5 239.2 12.8 2.3 17.3-5.6 17.3-12.1 0-6.2-.3-40.4-.3-61.4 0 0-70 15-84.7-29.8 0 0-11.4-29.1-27.8-36.6 0 0-22.9-15.7 1.6-15.4 0 0 24.9 2 38.6 25.8 21.9 38.6 58.6 27.5 72.9 20.9 2.3-16 8.8-27.1 16-33.7-55.9-6.2-112.3-14.3-112.3-110.5 0-27.5 7.6-41.3 23.6-58.9-2.6-6.5-11.1-33.3 2.6-67.9 20.9-6.5 69 27 69 27 20-5.6 41.5-8.5 62.8-8.5s42.8 2.9 62.8 8.5c0 0 48.1-33.6 69-27 13.7 34.7 5.2 61.4 2.6 67.9 16 17.7 25.8 31.5 25.8 58.9 0 96.5-58.9 104.2-114.8 110.5 9.2 7.9 17 22.9 17 46.4 0 33.7-.3 75.4-.3 83.6 0 6.5 4.6 14.4 17.3 12.1C428.2 457.8 496 362.9 496 252 496 113.3 383.5 8 244.8 8zM97.2 352.9c-1.3 1-1 3.3 .7 5.2 1.6 1.6 3.9 2.3 5.2 1 1.3-1 1-3.3-.7-5.2-1.6-1.6-3.9-2.3-5.2-1zm-10.8-8.1c-.7 1.3 .3 2.9 2.3 3.9 1.6 1 3.6 .7 4.3-.7 .7-1.3-.3-2.9-2.3-3.9-2-.6-3.6-.3-4.3 .7zm32.4 35.6c-1.6 1.3-1 4.3 1.3 6.2 2.3 2.3 5.2 2.6 6.5 1 1.3-1.3 .7-4.3-1.3-6.2-2.2-2.3-5.2-2.6-6.5-1zm-11.4-14.7c-1.6 1-1.6 3.6 0 5.9 1.6 2.3 4.3 3.3 5.6 2.3 1.6-1.3 1.6-3.9 0-6.2-1.4-2.3-4-3.3-5.6-2z\"/></svg>\n",
    "<a href=\"https://github.com/AmbiqAI/heartkit/blob/main/docs/guides/ecg-foundation-model.ipynb\" class=\"md-content__button md-icon\" style=\"color: #2094F3;\">\n",
    "    GitHub source\n",
    "</a>\n",
    "\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# ECG Foundation Model\n",
    "\n",
    "__Date created:__ 2024/07/25 \n",
    "\n",
    "__Last Modified:__ 2024/08/14 \n",
    "\n",
    "__Description:__ Train, evaluate, and export an ECG foundation model\n",
    "\n",
    "## Overview \n",
    "\n",
    "This notebook demonstrates creating a foundation model for raw ECG signals. By creating a foundation model, we can create small, down-stream classification models.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#!pip install -q --disable-pip-version-check heartkit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-08-16 18:53:16.603880: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:485] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n",
      "2024-08-16 18:53:16.611916: E external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:8454] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n",
      "2024-08-16 18:53:16.614257: E external/local_xla/xla/stream_executor/cuda/cuda_blas.cc:1452] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2' # 3\n",
    "import contextlib\n",
    "from pathlib import Path\n",
    "import tempfile\n",
    "import keras\n",
    "import heartkit as hk\n",
    "import tensorflow as tf\n",
    "import numpy as np\n",
    "import neuralspot_edge as nse\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.manifold import TSNE"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Constants\n",
    "\n",
    "Here we provide the constants that we will use throughout the guide. For better performance, adjust parameters such as `BATCH_SIZE`, `EPOCHS`, and `LEARNING_RATE`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# File paths\n",
    "datasets_dir = Path(os.getenv('HK_DATASET_PATH', './datasets'))\n",
    "job_dir = Path(tempfile.gettempdir()) / \"hk-foundation\"\n",
    "model_file = job_dir / \"model.keras\"\n",
    "val_file = job_dir / \"val.pkl\"\n",
    "\n",
    "# Data settings\n",
    "sampling_rate = 100 # 100 Hz\n",
    "input_size = 1000 # 10 seconds\n",
    "frame_size = 800 # 8 seconds\n",
    "\n",
    "# Training settings\n",
    "batch_size = 1024        # Batch size for training\n",
    "buffer_size = 2000       # How many samples are shuffled each epoch\n",
    "epochs = 150             # Increase this to 100+\n",
    "steps_per_epoch = 25     # # Steps per epoch (must set since ds has unknown size)\n",
    "samples_per_patient = 1  # Number of samples per patient\n",
    "val_metric = \"loss\"      # Metric to monitor for early stopping\n",
    "val_mode = \"min\"         # Mode for early stopping\n",
    "val_size = 10000        # Number of samples used for validation\n",
    "learning_rate = 1e-3     # Learning rate for Adam optimizer\n",
    "epsilon = 0.001\n",
    "\n",
    "# Model settings\n",
    "projection_width = 128\n",
    "temperature = 0.1\n",
    "\n",
    "# Other settings\n",
    "seed = 42                       # Seed for reproducibility\n",
    "verbose = 1                     # Verbosity level\n",
    "plot_theme = hk.utils.dark_theme\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #000080; text-decoration-color: #000080\">INFO    </span> Job directory: <span style=\"color: #800080; text-decoration-color: #800080\">/tmp/</span><span style=\"color: #ff00ff; text-decoration-color: #ff00ff\">hk-foundation</span>                                                          <a href=\"file:///tmp/ipykernel_712291/1079341004.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">1079341004.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///tmp/ipykernel_712291/1079341004.py#6\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">6</span></a>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[34mINFO    \u001b[0m Job directory: \u001b[35m/tmp/\u001b[0m\u001b[95mhk-foundation\u001b[0m                                                          \u001b]8;id=625876;file:///tmp/ipykernel_712291/1079341004.py\u001b\\\u001b[2m1079341004.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=4474;file:///tmp/ipykernel_712291/1079341004.py#6\u001b\\\u001b[2m6\u001b[0m\u001b]8;;\u001b\\\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "nse.utils.silence_tensorflow()\n",
    "hk.utils.setup_plotting(plot_theme)\n",
    "logger = nse.utils.setup_logger(__name__, level=verbose)\n",
    "\n",
    "os.makedirs(job_dir, exist_ok=True)\n",
    "logger.info(f\"Job directory: {job_dir}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Configure datasets\n",
    "\n",
    "We are going to train our model using two large datasets: the PTB-XL dataset and the large-scale arrhythmia dataset. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "datasets = [\n",
    "    hk.NamedParams(\n",
    "        name=\"lsad\",\n",
    "        params=dict(\n",
    "            path=datasets_dir / \"lsad\"\n",
    "        )\n",
    "    ),\n",
    "    hk.NamedParams(\n",
    "        name=\"ptbxl\",\n",
    "        params=dict(\n",
    "            path=datasets_dir / \"ptbxl\"\n",
    "        )\n",
    "    ),\n",
    "]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Download datasets\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "for dataset in datasets:\n",
    "    ds = hk.DatasetFactory.get(dataset.name)(\n",
    "        **dataset.params\n",
    "    )\n",
    "    ds.download(force=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create data pipeline\n",
    "\n",
    "Next, we will create a `tf.data` pipeline by performing the following steps on each dataset: \n",
    "* Loading dataset class handler \n",
    "* Leverage task specific data loader for given dataset\n",
    "* Splittiing the dataset into training and validation sets\n",
    "* Creating `tf.data.Dataset` objects for training and validation\n",
    "\n",
    "After creating all the `tf.data.Dataset` objects, we will merge them into a single dataset for training and validation. \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load datasets\n",
    "dsets = [hk.DatasetFactory.get(ds.name)(**ds.params) for ds in datasets]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n",
      "I0000 00:00:1723834403.812869  712291 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n",
      "I0000 00:00:1723834403.835711  712291 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n",
      "I0000 00:00:1723834403.835842  712291 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n",
      "I0000 00:00:1723834403.837216  712291 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n",
      "I0000 00:00:1723834403.837303  712291 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n",
      "I0000 00:00:1723834403.837349  712291 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n",
      "I0000 00:00:1723834403.890424  712291 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n",
      "I0000 00:00:1723834403.890527  712291 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n",
      "I0000 00:00:1723834403.890585  712291 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n"
     ]
    }
   ],
   "source": [
    "dset_weights = np.array([0.5, 0.5])\n",
    "\n",
    "train_datasets = []\n",
    "val_datasets = []\n",
    "for ds in dsets:\n",
    "\n",
    "    # Create dataloader specific to dataset\n",
    "    dataloader = hk.tasks.foundation.FoundationTaskFactory.get(ds.name)(\n",
    "        ds=ds,\n",
    "        frame_size=frame_size,\n",
    "        sampling_rate=sampling_rate,\n",
    "    )\n",
    "\n",
    "    # Split patients into train and validation sets\n",
    "    train_patients, val_patients = dataloader.split_train_val_patients()\n",
    "\n",
    "    # Create train dataset\n",
    "    train_ds = dataloader.create_dataloader(\n",
    "        patient_ids=train_patients,\n",
    "        samples_per_patient=samples_per_patient,\n",
    "        shuffle=True\n",
    "    )\n",
    "\n",
    "    # Create validation dataset\n",
    "    val_ds = dataloader.create_dataloader(\n",
    "        patient_ids=val_patients,\n",
    "        samples_per_patient=samples_per_patient,\n",
    "        shuffle=False\n",
    "    )\n",
    "    train_datasets.append(train_ds)\n",
    "    val_datasets.append(val_ds)\n",
    "# END FOR\n",
    "\n",
    "# Combine datasets\n",
    "train_ds = tf.data.Dataset.sample_from_datasets(train_datasets, weights=dset_weights)\n",
    "val_ds = tf.data.Dataset.sample_from_datasets(val_datasets, weights=dset_weights)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visualize the data\n",
    "\n",
    "Let's visualize a sample ECG signal from the synthetic dataset. Note this contains no noise or artifacts. Augmentations will be applied later to generate noisy samples for training."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 900x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ecg1, ecg2 = next(iter(train_ds))\n",
    "ecg1, ecg2 = ecg1.numpy().squeeze(), ecg2.numpy().squeeze()\n",
    "\n",
    "ts = np.arange(0, len(ecg1)) / sampling_rate\n",
    "fig, ax = plt.subplots(1, 1, figsize=(9, 4))\n",
    "ax.plot(ts, ecg1, color=plot_theme.primary_color, lw=3)\n",
    "ax.plot(ts, ecg2, color=plot_theme.secondary_color, lw=3)\n",
    "fig.suptitle(\"Raw ECG Signal\")\n",
    "ax.set_xlabel(\"Time (s)\")\n",
    "ax.set_ylabel(\"Amplitude\")\n",
    "fig.tight_layout()\n",
    "fig.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create augmentation pipeline\n",
    "\n",
    "To enable self-supervised training to learn useful features from raw ECG signals, we need to create an augmentation pipeline. Each sample will be augmented into two different ways. Using contrastive learning, the model should generate features that are similar for the same sample and different for different samples. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "nstdb = hk.datasets.nstdb.NstdbNoise(target_rate=sampling_rate)\n",
    "noises = np.hstack((nstdb.get_noise(noise_type=\"bw\"), nstdb.get_noise(noise_type=\"ma\"), nstdb.get_noise(noise_type=\"em\")))\n",
    "noises = noises.astype(np.float32)\n",
    "\n",
    "preprocessor = nse.layers.preprocessing.LayerNormalization1D(\n",
    "    epsilon=epsilon,\n",
    "    name=\"LayerNormalization\"\n",
    ")\n",
    "\n",
    "augmenter = nse.layers.preprocessing.AugmentationPipeline(\n",
    "    layers=[\n",
    "        nse.layers.preprocessing.RandomNoiseDistortion1D(\n",
    "            sample_rate=sampling_rate,\n",
    "            amplitude=(0, 1.0),\n",
    "            frequency=(0.5, 1.5),\n",
    "            name=\"BaselineWander\"\n",
    "        ),\n",
    "        nse.layers.preprocessing.RandomSineWave(\n",
    "            sample_rate=sampling_rate,\n",
    "            amplitude=(0, 0.05),\n",
    "            frequency=(45, 50),\n",
    "            name=\"PowerlineNoise\"\n",
    "        ),\n",
    "        nse.layers.preprocessing.AmplitudeWarp(\n",
    "            sample_rate=sampling_rate,\n",
    "            amplitude=(0.9, 1.1),\n",
    "            frequency=(0.5, 1.5),\n",
    "            name=\"AmplitudeWarp\"\n",
    "        ),\n",
    "        nse.layers.preprocessing.RandomGaussianNoise1D(\n",
    "            factor=(0.05, 0.2),\n",
    "            name=\"GaussianNoise\"\n",
    "        ),\n",
    "        nse.layers.preprocessing.RandomBackgroundNoises1D(\n",
    "            noises=noises,\n",
    "            amplitude=(0.05, 0.2),\n",
    "            num_noises=2,\n",
    "            name=\"RandomBackgroundNoises\"\n",
    "        ),\n",
    "        nse.layers.preprocessing.RandomCutout1D(\n",
    "            factor=(0.01, 0.05),\n",
    "            cutouts=2,\n",
    "            fill_mode=\"constant\",\n",
    "            fill_value=0.0,\n",
    "            name=\"RandomCutout\"\n",
    "        ),\n",
    "        nse.layers.preprocessing.RandomCrop1D(\n",
    "            duration=frame_size,\n",
    "            name=\"RandomCrop\",\n",
    "            auto_vectorize=True\n",
    "        )\n",
    "    ],\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualize augmented pair"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 900x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "aug_ecg1 = augmenter(preprocessor(keras.ops.convert_to_tensor(np.reshape(ecg1, (1, -1, 1)))), training=True)\n",
    "aug_ecg1 = aug_ecg1.numpy().squeeze()\n",
    "\n",
    "aug_ecg2 = augmenter(preprocessor(keras.ops.convert_to_tensor(np.reshape(ecg2, (1, -1, 1)))), training=True)\n",
    "aug_ecg2 = aug_ecg2.numpy().squeeze()\n",
    "\n",
    "\n",
    "ts = np.arange(0, frame_size, 1) / sampling_rate\n",
    "\n",
    "fig, ax = plt.subplots(1, 1, figsize=(9, 4))\n",
    "plt.title(\"Augmented ECG\")\n",
    "plt.plot(ts, aug_ecg1, color=plot_theme.primary_color, lw=2)\n",
    "plt.plot(ts, aug_ecg2, color=plot_theme.secondary_color, lw=2)\n",
    "ax.set_xlabel(\"Time (s)\")\n",
    "ax.set_ylabel(\"Amplitude\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create full data pipeline w/ augmentation\n",
    "\n",
    "We will now create a full data pipeline by extended the original with shuffling, batching, augmentations, and prefetching.\n",
    "\n",
    "For validation, we will cache a subset of the validation data to speed up the evaluation process."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_ds = train_ds.shuffle(\n",
    "    buffer_size=buffer_size,\n",
    "    reshuffle_each_iteration=True,\n",
    ").batch(\n",
    "    batch_size=batch_size,\n",
    "    drop_remainder=True,\n",
    "    num_parallel_calls=tf.data.AUTOTUNE,\n",
    ").map(\n",
    "    lambda x1, x2: {\n",
    "        nse.trainers.SimCLRTrainer.SAMPLES: x1,\n",
    "        nse.trainers.SimCLRTrainer.AUG_SAMPLES_0: augmenter(preprocessor(x1), training=True),\n",
    "        nse.trainers.SimCLRTrainer.AUG_SAMPLES_1: augmenter(preprocessor(x2), training=True),\n",
    "    },\n",
    "    num_parallel_calls=tf.data.AUTOTUNE\n",
    ").prefetch(\n",
    "    tf.data.AUTOTUNE\n",
    ")\n",
    "\n",
    "val_ds = val_ds.batch(\n",
    "    batch_size=batch_size,\n",
    "    drop_remainder=True,\n",
    "    num_parallel_calls=tf.data.AUTOTUNE,\n",
    ").map(\n",
    "    lambda x1, x2: {\n",
    "        nse.trainers.SimCLRTrainer.SAMPLES: x1,\n",
    "        nse.trainers.SimCLRTrainer.AUG_SAMPLES_0: augmenter(preprocessor(x1), training=True),\n",
    "        nse.trainers.SimCLRTrainer.AUG_SAMPLES_1: augmenter(preprocessor(x2), training=True),\n",
    "    },\n",
    "    num_parallel_calls=tf.data.AUTOTUNE\n",
    ").prefetch(\n",
    "    tf.data.AUTOTUNE\n",
    ")\n",
    "\n",
    "# Cache the validation dataset\n",
    "val_ds = val_ds.take(val_size//batch_size).cache()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Define encoder model \n",
    "\n",
    "For this task, we are going to leverage a customized __EfficientNetV2__ model architecture for the encoder that is smaller and can handle 1D signals. The model consists of 5 main MBConv blocks with a global average pooling layer and a dense layer for classification."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "inputs = keras.Input(shape=(frame_size, 1), name=\"input\")\n",
    "\n",
    "encoder_params=dict(\n",
    "    input_filters=24,\n",
    "    input_kernel_size=(1, 9),\n",
    "    input_strides=(1, 2),\n",
    "    blocks=[\n",
    "        dict(filters=32, depth=2, kernel_size=(1, 9), strides=(1, 2), ex_ratio=1, se_ratio=4, norm=\"layer\"),\n",
    "        dict(filters=48, depth=2, kernel_size=(1, 9), strides=(1, 2), ex_ratio=1, se_ratio=4, norm=\"layer\"),\n",
    "        dict(filters=64, depth=2, kernel_size=(1, 9), strides=(1, 2), ex_ratio=1, se_ratio=4, norm=\"layer\"),\n",
    "        dict(filters=80, depth=1, kernel_size=(1, 9), strides=(1, 2), ex_ratio=1, se_ratio=4, norm=\"layer\"),\n",
    "        dict(filters=96, depth=1, kernel_size=(1, 9), strides=(1, 2), ex_ratio=1, se_ratio=4, norm=\"layer\"),\n",
    "    ],\n",
    "    output_filters=projection_width,\n",
    "    include_top=True,\n",
    ")\n",
    "\n",
    "encoder = nse.models.efficientnet.efficientnetv2_from_object(\n",
    "    x=inputs,\n",
    "    params=encoder_params,\n",
    "    num_classes=None\n",
    ")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visualize the model\n",
    "\n",
    "Let's view the encoder to understand the architecture better."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #000080; text-decoration-color: #000080\">INFO    </span> Model: <span style=\"color: #008000; text-decoration-color: #008000\">\"EfficientNetV2\"</span>                                                               <a href=\"file:///workspaces/heartkit/.venv/lib/python3.12/site-packages/keras/src/utils/summary_utils.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">summary_utils.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///workspaces/heartkit/.venv/lib/python3.12/site-packages/keras/src/utils/summary_utils.py#389\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">389</span></a>\n",
       "         ┏━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┓          <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">                    </span>\n",
       "         ┃ Layer <span style=\"font-weight: bold\">(</span>type<span style=\"font-weight: bold\">)</span>        ┃ Output Shape      ┃    Param # ┃ Connected to      ┃          <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">                    </span>\n",
       "         ┡━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━┩          <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">                    </span>\n",
       "         │ input <span style=\"font-weight: bold\">(</span>InputLayer<span style=\"font-weight: bold\">)</span>  │ <span style=\"font-weight: bold\">(</span><span style=\"color: #800080; text-decoration-color: #800080; font-style: italic\">None</span>, <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">800</span>, <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1</span><span style=\"font-weight: bold\">)</span>    │          <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0</span> │ -                 │          <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">                    </span>\n",
       "         ├─────────────────────┼───────────────────┼────────────┼───────────────────┤          <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">                    </span>\n",
       "         │ reshape <span style=\"font-weight: bold\">(</span>Reshape<span style=\"font-weight: bold\">)</span>   │ <span style=\"font-weight: bold\">(</span><span style=\"color: #800080; text-decoration-color: #800080; font-style: italic\">None</span>, <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1</span>, <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">800</span>, <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1</span><span style=\"font-weight: bold\">)</span> │          <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0</span> │ input<span style=\"font-weight: bold\">[</span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0</span><span style=\"font-weight: bold\">][</span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0</span><span style=\"font-weight: bold\">]</span>       │          <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">                    </span>\n",
       "         ├─────────────────────┼───────────────────┼────────────┼───────────────────┤          <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">                    </span>\n",
       "         │ stem.conv <span style=\"font-weight: bold\">(</span>Conv2D<span style=\"font-weight: bold\">)</span>  │ <span style=\"font-weight: bold\">(</span><span style=\"color: #800080; text-decoration-color: #800080; font-style: italic\">None</span>, <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1</span>, <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">400</span>,    │        <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">216</span> │ reshape<span style=\"font-weight: bold\">[</span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0</span><span style=\"font-weight: bold\">][</span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0</span><span style=\"font-weight: bold\">]</span>     │          <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">                    </span>\n",
       "         │                     │ <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">24</span><span style=\"font-weight: bold\">)</span>               │            │                   │          <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">                    </span>\n",
       "         ├─────────────────────┼───────────────────┼────────────┼───────────────────┤          <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">                    </span>\n",
       "         │ stem.bn             │ <span style=\"font-weight: bold\">(</span><span style=\"color: #800080; text-decoration-color: #800080; font-style: italic\">None</span>, <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1</span>, <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">400</span>,    │         <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">96</span> │ stem.conv<span style=\"font-weight: bold\">[</span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0</span><span style=\"font-weight: bold\">][</span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0</span><span style=\"font-weight: bold\">]</span>   │          <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">                    </span>\n",
       "         │ <span style=\"font-weight: bold\">(</span>BatchNormalizatio… │ <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">24</span><span style=\"font-weight: bold\">)</span>               │            │                   │          <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">                    </span>\n",
       "         ├─────────────────────┼───────────────────┼────────────┼───────────────────┤          <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">                    </span>\n",
       "         │ stem.act            │ <span style=\"font-weight: bold\">(</span><span style=\"color: #800080; text-decoration-color: #800080; font-style: italic\">None</span>, <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1</span>, <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">400</span>,    │          <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0</span> │ stem.bn<span style=\"font-weight: bold\">[</span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0</span><span style=\"font-weight: bold\">][</span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0</span><span style=\"font-weight: bold\">]</span>     │          <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">                    </span>\n",
       "         │ <span style=\"font-weight: bold\">(</span>Activation<span style=\"font-weight: bold\">)</span>        │ <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">24</span><span style=\"font-weight: bold\">)</span>               │            │                   │          <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">                    </span>\n",
       "         ├─────────────────────┼───────────────────┼────────────┼───────────────────┤          <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">                    </span>\n",
       "         │ stage1.mbconv1.dp   │ <span style=\"font-weight: bold\">(</span><span style=\"color: #800080; text-decoration-color: #800080; font-style: italic\">None</span>, <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1</span>, <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">400</span>,    │        <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">216</span> │ stem.act<span style=\"font-weight: bold\">[</span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0</span><span style=\"font-weight: bold\">][</span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0</span><span style=\"font-weight: bold\">]</span>    │          <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">                    </span>\n",
       "         │ <span style=\"font-weight: bold\">(</span>DepthwiseConv2D<span style=\"font-weight: bold\">)</span>   │ <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">24</span><span style=\"font-weight: bold\">)</span>               │            │                   │          <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">                    </span>\n",
       "         ├─────────────────────┼───────────────────┼────────────┼───────────────────┤          <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">                    </span>\n",
       "         │ stage1.mbconv1.dp.… │ <span style=\"font-weight: bold\">(</span><span style=\"color: #800080; text-decoration-color: #800080; font-style: italic\">None</span>, <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1</span>, <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">400</span>,    │         <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">96</span> │ stage1.mbconv1.d… │          <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">                    </span>\n",
       "         │ <span style=\"font-weight: bold\">(</span>BatchNormalizatio… │ <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">24</span><span style=\"font-weight: bold\">)</span>               │            │                   │          <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">                    </span>\n",
       "         ├─────────────────────┼───────────────────┼────────────┼───────────────────┤          <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">                    </span>\n",
       "         │ stage1.mbconv1.dp.… │ <span style=\"font-weight: bold\">(</span><span style=\"color: #800080; text-decoration-color: #800080; font-style: italic\">None</span>, <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1</span>, <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">400</span>,    │          <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0</span> │ stage1.mbconv1.d… │          <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">                    </span>\n",
       "         │ <span style=\"font-weight: bold\">(</span>Activation<span style=\"font-weight: bold\">)</span>        │ <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">24</span><span style=\"font-weight: bold\">)</span>               │            │                   │          <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">                    </span>\n",
       "         ├─────────────────────┼───────────────────┼────────────┼───────────────────┤          <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">                    </span>\n",
       "         │ max_pooling2d       │ <span style=\"font-weight: bold\">(</span><span style=\"color: #800080; text-decoration-color: #800080; font-style: italic\">None</span>, <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1</span>, <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">200</span>,    │          <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0</span> │ stage1.mbconv1.d… │          <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">                    </span>\n",
       "         │ <span style=\"font-weight: bold\">(</span>MaxPooling2D<span style=\"font-weight: bold\">)</span>      │ <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">24</span><span style=\"font-weight: bold\">)</span>               │            │                   │          <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">                    </span>\n",
       "         ├─────────────────────┼───────────────────┼────────────┼───────────────────┤          <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">                    </span>\n",
       "         │ stage1.mbconv1.se.… │ <span style=\"font-weight: bold\">(</span><span style=\"color: #800080; text-decoration-color: #800080; font-style: italic\">None</span>, <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1</span>, <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1</span>, <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">24</span><span style=\"font-weight: bold\">)</span>  │          <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0</span> │ max_pooling2d<span style=\"font-weight: bold\">[</span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0</span><span style=\"font-weight: bold\">]</span>… │          <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">                    </span>\n",
       "         │ <span style=\"font-weight: bold\">(</span>GlobalAveragePool… │                   │            │                   │          <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">                    </span>\n",
       "         ├─────────────────────┼───────────────────┼────────────┼───────────────────┤          <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">                    </span>\n",
       "         │ stage1.mbconv1.se.… │ <span style=\"font-weight: bold\">(</span><span style=\"color: #800080; text-decoration-color: #800080; font-style: italic\">None</span>, <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1</span>, <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1</span>, <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">6</span><span style=\"font-weight: bold\">)</span>   │        <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">150</span> │ stage1.mbconv1.s… │          <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">                    </span>\n",
       "         │ <span style=\"font-weight: bold\">(</span>Conv2D<span style=\"font-weight: bold\">)</span>            │                   │            │                   │          <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">                    </span>\n",
       "         └─────────────────────┴───────────────────┴────────────┴───────────────────┘          <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">                    </span>\n",
       "          Total params: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">57</span>,<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">066</span> <span style=\"font-weight: bold\">(</span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">222.91</span> KB<span style=\"font-weight: bold\">)</span>                                                     <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">                    </span>\n",
       "          Trainable params: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">55</span>,<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">050</span> <span style=\"font-weight: bold\">(</span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">215.04</span> KB<span style=\"font-weight: bold\">)</span>                                                 <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">                    </span>\n",
       "          Non-trainable params: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">2</span>,<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">016</span> <span style=\"font-weight: bold\">(</span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">7.88</span> KB<span style=\"font-weight: bold\">)</span>                                                <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">                    </span>\n",
       "                                                                                               <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">                    </span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[34mINFO    \u001b[0m Model: \u001b[32m\"EfficientNetV2\"\u001b[0m                                                               \u001b]8;id=445330;file:///workspaces/heartkit/.venv/lib/python3.12/site-packages/keras/src/utils/summary_utils.py\u001b\\\u001b[2msummary_utils.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=349863;file:///workspaces/heartkit/.venv/lib/python3.12/site-packages/keras/src/utils/summary_utils.py#389\u001b\\\u001b[2m389\u001b[0m\u001b]8;;\u001b\\\n",
       "         ┏━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┓          \u001b[2m                    \u001b[0m\n",
       "         ┃ Layer \u001b[1m(\u001b[0mtype\u001b[1m)\u001b[0m        ┃ Output Shape      ┃    Param # ┃ Connected to      ┃          \u001b[2m                    \u001b[0m\n",
       "         ┡━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━┩          \u001b[2m                    \u001b[0m\n",
       "         │ input \u001b[1m(\u001b[0mInputLayer\u001b[1m)\u001b[0m  │ \u001b[1m(\u001b[0m\u001b[3;35mNone\u001b[0m, \u001b[1;36m800\u001b[0m, \u001b[1;36m1\u001b[0m\u001b[1m)\u001b[0m    │          \u001b[1;36m0\u001b[0m │ -                 │          \u001b[2m                    \u001b[0m\n",
       "         ├─────────────────────┼───────────────────┼────────────┼───────────────────┤          \u001b[2m                    \u001b[0m\n",
       "         │ reshape \u001b[1m(\u001b[0mReshape\u001b[1m)\u001b[0m   │ \u001b[1m(\u001b[0m\u001b[3;35mNone\u001b[0m, \u001b[1;36m1\u001b[0m, \u001b[1;36m800\u001b[0m, \u001b[1;36m1\u001b[0m\u001b[1m)\u001b[0m │          \u001b[1;36m0\u001b[0m │ input\u001b[1m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1m]\u001b[0m\u001b[1m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1m]\u001b[0m       │          \u001b[2m                    \u001b[0m\n",
       "         ├─────────────────────┼───────────────────┼────────────┼───────────────────┤          \u001b[2m                    \u001b[0m\n",
       "         │ stem.conv \u001b[1m(\u001b[0mConv2D\u001b[1m)\u001b[0m  │ \u001b[1m(\u001b[0m\u001b[3;35mNone\u001b[0m, \u001b[1;36m1\u001b[0m, \u001b[1;36m400\u001b[0m,    │        \u001b[1;36m216\u001b[0m │ reshape\u001b[1m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1m]\u001b[0m\u001b[1m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1m]\u001b[0m     │          \u001b[2m                    \u001b[0m\n",
       "         │                     │ \u001b[1;36m24\u001b[0m\u001b[1m)\u001b[0m               │            │                   │          \u001b[2m                    \u001b[0m\n",
       "         ├─────────────────────┼───────────────────┼────────────┼───────────────────┤          \u001b[2m                    \u001b[0m\n",
       "         │ stem.bn             │ \u001b[1m(\u001b[0m\u001b[3;35mNone\u001b[0m, \u001b[1;36m1\u001b[0m, \u001b[1;36m400\u001b[0m,    │         \u001b[1;36m96\u001b[0m │ stem.conv\u001b[1m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1m]\u001b[0m\u001b[1m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1m]\u001b[0m   │          \u001b[2m                    \u001b[0m\n",
       "         │ \u001b[1m(\u001b[0mBatchNormalizatio… │ \u001b[1;36m24\u001b[0m\u001b[1m)\u001b[0m               │            │                   │          \u001b[2m                    \u001b[0m\n",
       "         ├─────────────────────┼───────────────────┼────────────┼───────────────────┤          \u001b[2m                    \u001b[0m\n",
       "         │ stem.act            │ \u001b[1m(\u001b[0m\u001b[3;35mNone\u001b[0m, \u001b[1;36m1\u001b[0m, \u001b[1;36m400\u001b[0m,    │          \u001b[1;36m0\u001b[0m │ stem.bn\u001b[1m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1m]\u001b[0m\u001b[1m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1m]\u001b[0m     │          \u001b[2m                    \u001b[0m\n",
       "         │ \u001b[1m(\u001b[0mActivation\u001b[1m)\u001b[0m        │ \u001b[1;36m24\u001b[0m\u001b[1m)\u001b[0m               │            │                   │          \u001b[2m                    \u001b[0m\n",
       "         ├─────────────────────┼───────────────────┼────────────┼───────────────────┤          \u001b[2m                    \u001b[0m\n",
       "         │ stage1.mbconv1.dp   │ \u001b[1m(\u001b[0m\u001b[3;35mNone\u001b[0m, \u001b[1;36m1\u001b[0m, \u001b[1;36m400\u001b[0m,    │        \u001b[1;36m216\u001b[0m │ stem.act\u001b[1m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1m]\u001b[0m\u001b[1m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1m]\u001b[0m    │          \u001b[2m                    \u001b[0m\n",
       "         │ \u001b[1m(\u001b[0mDepthwiseConv2D\u001b[1m)\u001b[0m   │ \u001b[1;36m24\u001b[0m\u001b[1m)\u001b[0m               │            │                   │          \u001b[2m                    \u001b[0m\n",
       "         ├─────────────────────┼───────────────────┼────────────┼───────────────────┤          \u001b[2m                    \u001b[0m\n",
       "         │ stage1.mbconv1.dp.… │ \u001b[1m(\u001b[0m\u001b[3;35mNone\u001b[0m, \u001b[1;36m1\u001b[0m, \u001b[1;36m400\u001b[0m,    │         \u001b[1;36m96\u001b[0m │ stage1.mbconv1.d… │          \u001b[2m                    \u001b[0m\n",
       "         │ \u001b[1m(\u001b[0mBatchNormalizatio… │ \u001b[1;36m24\u001b[0m\u001b[1m)\u001b[0m               │            │                   │          \u001b[2m                    \u001b[0m\n",
       "         ├─────────────────────┼───────────────────┼────────────┼───────────────────┤          \u001b[2m                    \u001b[0m\n",
       "         │ stage1.mbconv1.dp.… │ \u001b[1m(\u001b[0m\u001b[3;35mNone\u001b[0m, \u001b[1;36m1\u001b[0m, \u001b[1;36m400\u001b[0m,    │          \u001b[1;36m0\u001b[0m │ stage1.mbconv1.d… │          \u001b[2m                    \u001b[0m\n",
       "         │ \u001b[1m(\u001b[0mActivation\u001b[1m)\u001b[0m        │ \u001b[1;36m24\u001b[0m\u001b[1m)\u001b[0m               │            │                   │          \u001b[2m                    \u001b[0m\n",
       "         ├─────────────────────┼───────────────────┼────────────┼───────────────────┤          \u001b[2m                    \u001b[0m\n",
       "         │ max_pooling2d       │ \u001b[1m(\u001b[0m\u001b[3;35mNone\u001b[0m, \u001b[1;36m1\u001b[0m, \u001b[1;36m200\u001b[0m,    │          \u001b[1;36m0\u001b[0m │ stage1.mbconv1.d… │          \u001b[2m                    \u001b[0m\n",
       "         │ \u001b[1m(\u001b[0mMaxPooling2D\u001b[1m)\u001b[0m      │ \u001b[1;36m24\u001b[0m\u001b[1m)\u001b[0m               │            │                   │          \u001b[2m                    \u001b[0m\n",
       "         ├─────────────────────┼───────────────────┼────────────┼───────────────────┤          \u001b[2m                    \u001b[0m\n",
       "         │ stage1.mbconv1.se.… │ \u001b[1m(\u001b[0m\u001b[3;35mNone\u001b[0m, \u001b[1;36m1\u001b[0m, \u001b[1;36m1\u001b[0m, \u001b[1;36m24\u001b[0m\u001b[1m)\u001b[0m  │          \u001b[1;36m0\u001b[0m │ max_pooling2d\u001b[1m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1m]\u001b[0m… │          \u001b[2m                    \u001b[0m\n",
       "         │ \u001b[1m(\u001b[0mGlobalAveragePool… │                   │            │                   │          \u001b[2m                    \u001b[0m\n",
       "         ├─────────────────────┼───────────────────┼────────────┼───────────────────┤          \u001b[2m                    \u001b[0m\n",
       "         │ stage1.mbconv1.se.… │ \u001b[1m(\u001b[0m\u001b[3;35mNone\u001b[0m, \u001b[1;36m1\u001b[0m, \u001b[1;36m1\u001b[0m, \u001b[1;36m6\u001b[0m\u001b[1m)\u001b[0m   │        \u001b[1;36m150\u001b[0m │ stage1.mbconv1.s… │          \u001b[2m                    \u001b[0m\n",
       "         │ \u001b[1m(\u001b[0mConv2D\u001b[1m)\u001b[0m            │                   │            │                   │          \u001b[2m                    \u001b[0m\n",
       "         └─────────────────────┴───────────────────┴────────────┴───────────────────┘          \u001b[2m                    \u001b[0m\n",
       "          Total params: \u001b[1;36m57\u001b[0m,\u001b[1;36m066\u001b[0m \u001b[1m(\u001b[0m\u001b[1;36m222.91\u001b[0m KB\u001b[1m)\u001b[0m                                                     \u001b[2m                    \u001b[0m\n",
       "          Trainable params: \u001b[1;36m55\u001b[0m,\u001b[1;36m050\u001b[0m \u001b[1m(\u001b[0m\u001b[1;36m215.04\u001b[0m KB\u001b[1m)\u001b[0m                                                 \u001b[2m                    \u001b[0m\n",
       "          Non-trainable params: \u001b[1;36m2\u001b[0m,\u001b[1;36m016\u001b[0m \u001b[1m(\u001b[0m\u001b[1;36m7.88\u001b[0m KB\u001b[1m)\u001b[0m                                                \u001b[2m                    \u001b[0m\n",
       "                                                                                               \u001b[2m                    \u001b[0m\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #000080; text-decoration-color: #000080\">INFO    </span> Computation: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">4.17</span> MFLOPs                                                                    <a href=\"file:///tmp/ipykernel_712291/909537700.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">909537700.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///tmp/ipykernel_712291/909537700.py#3\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">3</span></a>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[34mINFO    \u001b[0m Computation: \u001b[1;36m4.17\u001b[0m MFLOPs                                                                    \u001b]8;id=614122;file:///tmp/ipykernel_712291/909537700.py\u001b\\\u001b[2m909537700.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=675398;file:///tmp/ipykernel_712291/909537700.py#3\u001b\\\u001b[2m3\u001b[0m\u001b]8;;\u001b\\\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "encoder.summary(print_fn=logger.info, layer_range=('input', encoder.layers[10].name))\n",
    "flops = nse.metrics.flops.get_flops(encoder, batch_size=1, fpath=os.devnull)\n",
    "logger.info(f\"Computation: {flops/1e6:0.2f} MFLOPs\")\n",
    "encoder_output = encoder(inputs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #000080; text-decoration-color: #000080\">INFO    </span> Model: <span style=\"color: #008000; text-decoration-color: #008000\">\"projector\"</span>                                                                    <a href=\"file:///workspaces/heartkit/.venv/lib/python3.12/site-packages/keras/src/utils/summary_utils.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">summary_utils.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///workspaces/heartkit/.venv/lib/python3.12/site-packages/keras/src/utils/summary_utils.py#389\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">389</span></a>\n",
       "         ┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓          <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">                    </span>\n",
       "         ┃ Layer <span style=\"font-weight: bold\">(</span>type<span style=\"font-weight: bold\">)</span>                    ┃ Output Shape           ┃       Param # ┃          <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">                    </span>\n",
       "         ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩          <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">                    </span>\n",
       "         │ keras_tensor_109CLONE           │ <span style=\"font-weight: bold\">(</span><span style=\"color: #800080; text-decoration-color: #800080; font-style: italic\">None</span>, <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">128</span><span style=\"font-weight: bold\">)</span>            │             <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0</span> │          <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">                    </span>\n",
       "         │ <span style=\"font-weight: bold\">(</span>InputLayer<span style=\"font-weight: bold\">)</span>                    │                        │               │          <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">                    </span>\n",
       "         ├─────────────────────────────────┼────────────────────────┼───────────────┤          <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">                    </span>\n",
       "         │ dense <span style=\"font-weight: bold\">(</span>Dense<span style=\"font-weight: bold\">)</span>                   │ <span style=\"font-weight: bold\">(</span><span style=\"color: #800080; text-decoration-color: #800080; font-style: italic\">None</span>, <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">128</span><span style=\"font-weight: bold\">)</span>            │        <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">16</span>,<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">512</span> │          <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">                    </span>\n",
       "         ├─────────────────────────────────┼────────────────────────┼───────────────┤          <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">                    </span>\n",
       "         │ dense_1 <span style=\"font-weight: bold\">(</span>Dense<span style=\"font-weight: bold\">)</span>                 │ <span style=\"font-weight: bold\">(</span><span style=\"color: #800080; text-decoration-color: #800080; font-style: italic\">None</span>, <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">128</span><span style=\"font-weight: bold\">)</span>            │        <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">16</span>,<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">512</span> │          <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">                    </span>\n",
       "         └─────────────────────────────────┴────────────────────────┴───────────────┘          <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">                    </span>\n",
       "          Total params: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">33</span>,<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">024</span> <span style=\"font-weight: bold\">(</span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">129.00</span> KB<span style=\"font-weight: bold\">)</span>                                                     <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">                    </span>\n",
       "          Trainable params: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">33</span>,<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">024</span> <span style=\"font-weight: bold\">(</span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">129.00</span> KB<span style=\"font-weight: bold\">)</span>                                                 <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">                    </span>\n",
       "          Non-trainable params: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0</span> <span style=\"font-weight: bold\">(</span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.00</span> B<span style=\"font-weight: bold\">)</span>                                                     <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">                    </span>\n",
       "                                                                                               <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">                    </span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[34mINFO    \u001b[0m Model: \u001b[32m\"projector\"\u001b[0m                                                                    \u001b]8;id=439076;file:///workspaces/heartkit/.venv/lib/python3.12/site-packages/keras/src/utils/summary_utils.py\u001b\\\u001b[2msummary_utils.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=568200;file:///workspaces/heartkit/.venv/lib/python3.12/site-packages/keras/src/utils/summary_utils.py#389\u001b\\\u001b[2m389\u001b[0m\u001b]8;;\u001b\\\n",
       "         ┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓          \u001b[2m                    \u001b[0m\n",
       "         ┃ Layer \u001b[1m(\u001b[0mtype\u001b[1m)\u001b[0m                    ┃ Output Shape           ┃       Param # ┃          \u001b[2m                    \u001b[0m\n",
       "         ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩          \u001b[2m                    \u001b[0m\n",
       "         │ keras_tensor_109CLONE           │ \u001b[1m(\u001b[0m\u001b[3;35mNone\u001b[0m, \u001b[1;36m128\u001b[0m\u001b[1m)\u001b[0m            │             \u001b[1;36m0\u001b[0m │          \u001b[2m                    \u001b[0m\n",
       "         │ \u001b[1m(\u001b[0mInputLayer\u001b[1m)\u001b[0m                    │                        │               │          \u001b[2m                    \u001b[0m\n",
       "         ├─────────────────────────────────┼────────────────────────┼───────────────┤          \u001b[2m                    \u001b[0m\n",
       "         │ dense \u001b[1m(\u001b[0mDense\u001b[1m)\u001b[0m                   │ \u001b[1m(\u001b[0m\u001b[3;35mNone\u001b[0m, \u001b[1;36m128\u001b[0m\u001b[1m)\u001b[0m            │        \u001b[1;36m16\u001b[0m,\u001b[1;36m512\u001b[0m │          \u001b[2m                    \u001b[0m\n",
       "         ├─────────────────────────────────┼────────────────────────┼───────────────┤          \u001b[2m                    \u001b[0m\n",
       "         │ dense_1 \u001b[1m(\u001b[0mDense\u001b[1m)\u001b[0m                 │ \u001b[1m(\u001b[0m\u001b[3;35mNone\u001b[0m, \u001b[1;36m128\u001b[0m\u001b[1m)\u001b[0m            │        \u001b[1;36m16\u001b[0m,\u001b[1;36m512\u001b[0m │          \u001b[2m                    \u001b[0m\n",
       "         └─────────────────────────────────┴────────────────────────┴───────────────┘          \u001b[2m                    \u001b[0m\n",
       "          Total params: \u001b[1;36m33\u001b[0m,\u001b[1;36m024\u001b[0m \u001b[1m(\u001b[0m\u001b[1;36m129.00\u001b[0m KB\u001b[1m)\u001b[0m                                                     \u001b[2m                    \u001b[0m\n",
       "          Trainable params: \u001b[1;36m33\u001b[0m,\u001b[1;36m024\u001b[0m \u001b[1m(\u001b[0m\u001b[1;36m129.00\u001b[0m KB\u001b[1m)\u001b[0m                                                 \u001b[2m                    \u001b[0m\n",
       "          Non-trainable params: \u001b[1;36m0\u001b[0m \u001b[1m(\u001b[0m\u001b[1;36m0.00\u001b[0m B\u001b[1m)\u001b[0m                                                     \u001b[2m                    \u001b[0m\n",
       "                                                                                               \u001b[2m                    \u001b[0m\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "projector_input = encoder_output\n",
    "projector_output = keras.layers.Dense(projection_width, activation=\"relu6\")(projector_input)\n",
    "projector_output = keras.layers.Dense(projection_width)(projector_output)\n",
    "projector = keras.Model(inputs=projector_input, outputs=projector_output, name=\"projector\")\n",
    "flops = nse.metrics.flops.get_flops(projector, batch_size=1, fpath=os.devnull)\n",
    "projector.summary(print_fn=logger.info)\n",
    "logger.debug(f\"Projector requires {flops/1e6:0.2f} MFLOPS\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create a SimCLR model to train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = nse.trainers.SimCLRTrainer(\n",
    "    encoder=encoder,\n",
    "    augmenter=None,  # We augment in the data pipeline\n",
    "    projector=projector,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Compile the model\n",
    "\n",
    "We will compile the model using Adam optimizer with cosine learning rate scheduler and custom cosine similarity loss function. We will also attach metrics and callbacks to monitor the training process.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_scheduler():\n",
    "    return keras.optimizers.schedules.CosineDecay(\n",
    "        initial_learning_rate=learning_rate,\n",
    "        decay_steps=steps_per_epoch * epochs,\n",
    "    )\n",
    "\n",
    "optimizer = keras.optimizers.Adam(get_scheduler())\n",
    "loss = nse.losses.simclr.SimCLRLoss(temperature=temperature)\n",
    "\n",
    "metrics = [\n",
    "    keras.metrics.MeanSquaredError(name=\"mse\"),\n",
    "    keras.metrics.CosineSimilarity(name=\"cos\"),\n",
    "]\n",
    "\n",
    "model_callbacks = [\n",
    "    keras.callbacks.EarlyStopping(\n",
    "        monitor=f\"val_{val_metric}\",\n",
    "        patience=max(int(0.25 * epochs), 1),\n",
    "        mode=val_mode,\n",
    "        restore_best_weights=True,\n",
    "        verbose=verbose - 1\n",
    "    ),\n",
    "    keras.callbacks.ModelCheckpoint(\n",
    "        filepath=str(model_file),\n",
    "        monitor=f\"val_{val_metric}\",\n",
    "        save_best_only=True,\n",
    "        mode=val_mode,\n",
    "        verbose=verbose - 1\n",
    "    ),\n",
    "    keras.callbacks.CSVLogger(job_dir / \"history.csv\"),\n",
    "]\n",
    "if nse.utils.env_flag(\"TENSORBOARD\"):\n",
    "    model_callbacks.append(\n",
    "        keras.callbacks.TensorBoard(\n",
    "            log_dir=job_dir,\n",
    "            write_steps_per_second=True,\n",
    "        )\n",
    "    )\n",
    "\n",
    "model.compile(\n",
    "    encoder_optimizer=optimizer,\n",
    "    encoder_loss=loss,\n",
    "    encoder_metrics=metrics,\n",
    ")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Train the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/150\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-08-16 18:54:13.839587: E tensorflow/core/util/util.cc:131] oneDNN supports DT_INT32 only on platforms with AVX-512. Falling back to the default Eigen-based implementation if present.\n",
      "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n",
      "I0000 00:00:1723834463.457755  712486 service.cc:146] XLA service 0x78321c02f130 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n",
      "I0000 00:00:1723834463.457771  712486 service.cc:154]   StreamExecutor device (0): NVIDIA GeForce RTX 4090, Compute Capability 8.9\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m 1/25\u001b[0m \u001b[37m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m13:39\u001b[0m 34s/step - cos: 0.5956 - loss: 15.6336 - mse: 0.2352"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "I0000 00:00:1723834487.410060  712486 device_compiler.h:188] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m67s\u001b[0m 1s/step - cos: 0.6157 - loss: 14.9098 - mse: 0.2319 - val_cos: 0.6770 - val_loss: 12.6894 - val_mse: 0.2770\n",
      "Epoch 2/150\n",
      "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 228ms/step - cos: 0.6928 - loss: 12.2036 - mse: 0.2814 - val_cos: 0.7274 - val_loss: 11.2915 - val_mse: 0.2797\n",
      "Epoch 3/150\n",
      "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 183ms/step - cos: 0.7322 - loss: 11.1098 - mse: 0.2783 - val_cos: 0.7428 - val_loss: 10.5851 - val_mse: 0.2743\n",
      "Epoch 4/150\n",
      "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 185ms/step - cos: 0.7449 - loss: 10.4056 - mse: 0.2715 - val_cos: 0.7517 - val_loss: 9.9517 - val_mse: 0.2724\n",
      "Epoch 5/150\n",
      "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 184ms/step - cos: 0.7523 - loss: 9.8387 - mse: 0.2707 - val_cos: 0.7568 - val_loss: 9.5624 - val_mse: 0.2703\n",
      "Epoch 6/150\n",
      "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 183ms/step - cos: 0.7548 - loss: 9.5425 - mse: 0.2690 - val_cos: 0.7591 - val_loss: 9.2802 - val_mse: 0.2633\n",
      "Epoch 7/150\n",
      "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 179ms/step - cos: 0.7587 - loss: 9.2489 - mse: 0.2617 - val_cos: 0.7604 - val_loss: 9.0665 - val_mse: 0.2585\n",
      "Epoch 8/150\n",
      "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 185ms/step - cos: 0.7604 - loss: 9.0068 - mse: 0.2579 - val_cos: 0.7623 - val_loss: 8.8123 - val_mse: 0.2564\n",
      "Epoch 9/150\n",
      "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 182ms/step - cos: 0.7618 - loss: 8.7503 - mse: 0.2550 - val_cos: 0.7628 - val_loss: 8.5923 - val_mse: 0.2538\n",
      "Epoch 10/150\n",
      "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 179ms/step - cos: 0.7621 - loss: 8.5523 - mse: 0.2549 - val_cos: 0.7622 - val_loss: 8.4131 - val_mse: 0.2523\n",
      "Epoch 11/150\n",
      "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 186ms/step - cos: 0.7624 - loss: 8.3957 - mse: 0.2511 - val_cos: 0.7635 - val_loss: 8.2374 - val_mse: 0.2495\n",
      "Epoch 12/150\n",
      "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 181ms/step - cos: 0.7637 - loss: 8.2014 - mse: 0.2498 - val_cos: 0.7641 - val_loss: 8.0899 - val_mse: 0.2478\n",
      "Epoch 13/150\n",
      "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 181ms/step - cos: 0.7639 - loss: 8.0752 - mse: 0.2456 - val_cos: 0.7645 - val_loss: 7.9631 - val_mse: 0.2451\n",
      "Epoch 14/150\n",
      "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 177ms/step - cos: 0.7638 - loss: 7.9306 - mse: 0.2457 - val_cos: 0.7665 - val_loss: 7.8171 - val_mse: 0.2403\n",
      "Epoch 15/150\n",
      "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 182ms/step - cos: 0.7642 - loss: 7.8377 - mse: 0.2410 - val_cos: 0.7663 - val_loss: 7.7359 - val_mse: 0.2385\n",
      "Epoch 16/150\n",
      "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 185ms/step - cos: 0.7658 - loss: 7.6886 - mse: 0.2378 - val_cos: 0.7676 - val_loss: 7.6044 - val_mse: 0.2350\n",
      "Epoch 17/150\n",
      "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 188ms/step - cos: 0.7643 - loss: 7.6359 - mse: 0.2369 - val_cos: 0.7659 - val_loss: 7.5199 - val_mse: 0.2345\n",
      "Epoch 18/150\n",
      "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 184ms/step - cos: 0.7660 - loss: 7.5126 - mse: 0.2329 - val_cos: 0.7680 - val_loss: 7.4207 - val_mse: 0.2301\n",
      "Epoch 19/150\n",
      "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 177ms/step - cos: 0.7651 - loss: 7.4191 - mse: 0.2304 - val_cos: 0.7682 - val_loss: 7.3130 - val_mse: 0.2268\n",
      "Epoch 20/150\n",
      "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 182ms/step - cos: 0.7651 - loss: 7.3419 - mse: 0.2291 - val_cos: 0.7664 - val_loss: 7.2225 - val_mse: 0.2272\n",
      "Epoch 21/150\n",
      "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 179ms/step - cos: 0.7657 - loss: 7.2691 - mse: 0.2277 - val_cos: 0.7665 - val_loss: 7.1630 - val_mse: 0.2245\n",
      "Epoch 22/150\n",
      "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 182ms/step - cos: 0.7640 - loss: 7.2177 - mse: 0.2248 - val_cos: 0.7662 - val_loss: 7.0724 - val_mse: 0.2219\n",
      "Epoch 23/150\n",
      "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 181ms/step - cos: 0.7679 - loss: 7.0468 - mse: 0.2195 - val_cos: 0.7680 - val_loss: 6.9664 - val_mse: 0.2184\n",
      "Epoch 24/150\n",
      "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 178ms/step - cos: 0.7667 - loss: 6.9840 - mse: 0.2171 - val_cos: 0.7669 - val_loss: 6.9237 - val_mse: 0.2178\n",
      "Epoch 25/150\n",
      "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 181ms/step - cos: 0.7662 - loss: 6.9243 - mse: 0.2169 - val_cos: 0.7666 - val_loss: 6.8773 - val_mse: 0.2136\n",
      "Epoch 26/150\n",
      "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 180ms/step - cos: 0.7655 - loss: 6.8518 - mse: 0.2143 - val_cos: 0.7668 - val_loss: 6.7758 - val_mse: 0.2124\n",
      "Epoch 27/150\n",
      "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 181ms/step - cos: 0.7667 - loss: 6.7623 - mse: 0.2110 - val_cos: 0.7664 - val_loss: 6.7287 - val_mse: 0.2101\n",
      "Epoch 28/150\n",
      "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 184ms/step - cos: 0.7676 - loss: 6.7556 - mse: 0.2077 - val_cos: 0.7678 - val_loss: 6.6686 - val_mse: 0.2059\n",
      "Epoch 29/150\n",
      "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 191ms/step - cos: 0.7671 - loss: 6.6939 - mse: 0.2065 - val_cos: 0.7670 - val_loss: 6.6024 - val_mse: 0.2012\n",
      "Epoch 30/150\n",
      "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 186ms/step - cos: 0.7660 - loss: 6.6050 - mse: 0.2017 - val_cos: 0.7678 - val_loss: 6.5662 - val_mse: 0.1994\n",
      "Epoch 31/150\n",
      "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 180ms/step - cos: 0.7667 - loss: 6.5798 - mse: 0.2007 - val_cos: 0.7677 - val_loss: 6.5317 - val_mse: 0.1979\n",
      "Epoch 32/150\n",
      "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 180ms/step - cos: 0.7669 - loss: 6.5304 - mse: 0.1988 - val_cos: 0.7691 - val_loss: 6.4457 - val_mse: 0.1951\n",
      "Epoch 33/150\n",
      "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 184ms/step - cos: 0.7671 - loss: 6.4863 - mse: 0.1965 - val_cos: 0.7678 - val_loss: 6.4010 - val_mse: 0.1941\n",
      "Epoch 34/150\n",
      "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 185ms/step - cos: 0.7666 - loss: 6.4082 - mse: 0.1940 - val_cos: 0.7678 - val_loss: 6.3757 - val_mse: 0.1933\n",
      "Epoch 35/150\n",
      "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 189ms/step - cos: 0.7677 - loss: 6.3730 - mse: 0.1909 - val_cos: 0.7692 - val_loss: 6.3082 - val_mse: 0.1881\n",
      "Epoch 36/150\n",
      "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 177ms/step - cos: 0.7677 - loss: 6.3429 - mse: 0.1880 - val_cos: 0.7681 - val_loss: 6.2834 - val_mse: 0.1878\n",
      "Epoch 37/150\n",
      "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 179ms/step - cos: 0.7671 - loss: 6.2941 - mse: 0.1861 - val_cos: 0.7697 - val_loss: 6.2232 - val_mse: 0.1849\n",
      "Epoch 38/150\n",
      "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 178ms/step - cos: 0.7670 - loss: 6.2765 - mse: 0.1862 - val_cos: 0.7684 - val_loss: 6.1971 - val_mse: 0.1828\n",
      "Epoch 39/150\n",
      "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 179ms/step - cos: 0.7664 - loss: 6.2457 - mse: 0.1831 - val_cos: 0.7686 - val_loss: 6.1664 - val_mse: 0.1812\n",
      "Epoch 40/150\n",
      "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 179ms/step - cos: 0.7698 - loss: 6.1896 - mse: 0.1797 - val_cos: 0.7696 - val_loss: 6.1331 - val_mse: 0.1777\n",
      "Epoch 41/150\n",
      "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 178ms/step - cos: 0.7670 - loss: 6.1657 - mse: 0.1788 - val_cos: 0.7701 - val_loss: 6.1057 - val_mse: 0.1760\n",
      "Epoch 42/150\n",
      "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 180ms/step - cos: 0.7690 - loss: 6.0656 - mse: 0.1760 - val_cos: 0.7693 - val_loss: 6.0554 - val_mse: 0.1738\n",
      "Epoch 43/150\n",
      "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 178ms/step - cos: 0.7682 - loss: 6.0856 - mse: 0.1745 - val_cos: 0.7676 - val_loss: 6.0448 - val_mse: 0.1722\n",
      "Epoch 44/150\n",
      "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 180ms/step - cos: 0.7665 - loss: 6.0528 - mse: 0.1724 - val_cos: 0.7683 - val_loss: 6.0189 - val_mse: 0.1710\n",
      "Epoch 45/150\n",
      "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 186ms/step - cos: 0.7691 - loss: 6.0253 - mse: 0.1699 - val_cos: 0.7685 - val_loss: 5.9979 - val_mse: 0.1665\n",
      "Epoch 46/150\n",
      "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 179ms/step - cos: 0.7679 - loss: 5.9833 - mse: 0.1665 - val_cos: 0.7681 - val_loss: 5.9251 - val_mse: 0.1675\n",
      "Epoch 47/150\n",
      "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 175ms/step - cos: 0.7680 - loss: 5.9603 - mse: 0.1664 - val_cos: 0.7698 - val_loss: 5.9433 - val_mse: 0.1651\n",
      "Epoch 48/150\n",
      "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 182ms/step - cos: 0.7701 - loss: 5.9152 - mse: 0.1653 - val_cos: 0.7703 - val_loss: 5.9054 - val_mse: 0.1632\n",
      "Epoch 49/150\n",
      "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 180ms/step - cos: 0.7682 - loss: 5.8829 - mse: 0.1632 - val_cos: 0.7692 - val_loss: 5.8782 - val_mse: 0.1611\n",
      "Epoch 50/150\n",
      "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 182ms/step - cos: 0.7683 - loss: 5.8843 - mse: 0.1602 - val_cos: 0.7705 - val_loss: 5.8711 - val_mse: 0.1598\n",
      "Epoch 51/150\n",
      "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 178ms/step - cos: 0.7687 - loss: 5.8453 - mse: 0.1596 - val_cos: 0.7680 - val_loss: 5.8498 - val_mse: 0.1603\n",
      "Epoch 52/150\n",
      "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 179ms/step - cos: 0.7685 - loss: 5.8001 - mse: 0.1577 - val_cos: 0.7699 - val_loss: 5.7597 - val_mse: 0.1563\n",
      "Epoch 53/150\n",
      "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 173ms/step - cos: 0.7685 - loss: 5.7991 - mse: 0.1569 - val_cos: 0.7682 - val_loss: 5.7875 - val_mse: 0.1550\n",
      "Epoch 54/150\n",
      "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 176ms/step - cos: 0.7680 - loss: 5.7853 - mse: 0.1547 - val_cos: 0.7707 - val_loss: 5.7683 - val_mse: 0.1524\n",
      "Epoch 55/150\n",
      "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 179ms/step - cos: 0.7691 - loss: 5.7863 - mse: 0.1526 - val_cos: 0.7705 - val_loss: 5.7501 - val_mse: 0.1514\n",
      "Epoch 56/150\n",
      "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 179ms/step - cos: 0.7692 - loss: 5.7813 - mse: 0.1511 - val_cos: 0.7694 - val_loss: 5.7335 - val_mse: 0.1502\n",
      "Epoch 57/150\n",
      "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 179ms/step - cos: 0.7699 - loss: 5.7194 - mse: 0.1498 - val_cos: 0.7694 - val_loss: 5.7055 - val_mse: 0.1492\n",
      "Epoch 58/150\n",
      "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 181ms/step - cos: 0.7704 - loss: 5.6757 - mse: 0.1483 - val_cos: 0.7700 - val_loss: 5.6847 - val_mse: 0.1472\n",
      "Epoch 59/150\n",
      "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 181ms/step - cos: 0.7690 - loss: 5.7145 - mse: 0.1485 - val_cos: 0.7699 - val_loss: 5.6508 - val_mse: 0.1456\n",
      "Epoch 60/150\n",
      "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 180ms/step - cos: 0.7673 - loss: 5.6932 - mse: 0.1473 - val_cos: 0.7707 - val_loss: 5.6501 - val_mse: 0.1436\n",
      "Epoch 61/150\n",
      "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 180ms/step - cos: 0.7694 - loss: 5.6243 - mse: 0.1447 - val_cos: 0.7689 - val_loss: 5.6231 - val_mse: 0.1428\n",
      "Epoch 62/150\n",
      "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 180ms/step - cos: 0.7684 - loss: 5.6316 - mse: 0.1423 - val_cos: 0.7688 - val_loss: 5.5892 - val_mse: 0.1425\n",
      "Epoch 63/150\n",
      "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 177ms/step - cos: 0.7677 - loss: 5.6548 - mse: 0.1434 - val_cos: 0.7710 - val_loss: 5.5681 - val_mse: 0.1399\n",
      "Epoch 64/150\n",
      "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 174ms/step - cos: 0.7680 - loss: 5.6244 - mse: 0.1421 - val_cos: 0.7698 - val_loss: 5.5903 - val_mse: 0.1400\n",
      "Epoch 65/150\n",
      "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 180ms/step - cos: 0.7681 - loss: 5.6289 - mse: 0.1406 - val_cos: 0.7687 - val_loss: 5.5534 - val_mse: 0.1409\n",
      "Epoch 66/150\n",
      "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 177ms/step - cos: 0.7688 - loss: 5.5736 - mse: 0.1403 - val_cos: 0.7702 - val_loss: 5.5605 - val_mse: 0.1376\n",
      "Epoch 67/150\n",
      "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 179ms/step - cos: 0.7700 - loss: 5.5189 - mse: 0.1380 - val_cos: 0.7702 - val_loss: 5.5123 - val_mse: 0.1363\n",
      "Epoch 68/150\n",
      "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 175ms/step - cos: 0.7687 - loss: 5.5515 - mse: 0.1369 - val_cos: 0.7691 - val_loss: 5.5241 - val_mse: 0.1370\n",
      "Epoch 69/150\n",
      "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 181ms/step - cos: 0.7702 - loss: 5.5545 - mse: 0.1357 - val_cos: 0.7699 - val_loss: 5.4955 - val_mse: 0.1362\n",
      "Epoch 70/150\n",
      "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 179ms/step - cos: 0.7690 - loss: 5.4659 - mse: 0.1352 - val_cos: 0.7703 - val_loss: 5.4853 - val_mse: 0.1337\n",
      "Epoch 71/150\n",
      "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 179ms/step - cos: 0.7681 - loss: 5.4991 - mse: 0.1344 - val_cos: 0.7683 - val_loss: 5.4826 - val_mse: 0.1333\n",
      "Epoch 72/150\n",
      "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 177ms/step - cos: 0.7681 - loss: 5.4836 - mse: 0.1327 - val_cos: 0.7693 - val_loss: 5.4592 - val_mse: 0.1316\n",
      "Epoch 73/150\n",
      "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 178ms/step - cos: 0.7702 - loss: 5.4963 - mse: 0.1315 - val_cos: 0.7706 - val_loss: 5.4468 - val_mse: 0.1308\n",
      "Epoch 74/150\n",
      "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 178ms/step - cos: 0.7696 - loss: 5.3915 - mse: 0.1302 - val_cos: 0.7698 - val_loss: 5.4245 - val_mse: 0.1298\n",
      "Epoch 75/150\n",
      "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 178ms/step - cos: 0.7706 - loss: 5.4288 - mse: 0.1288 - val_cos: 0.7695 - val_loss: 5.3944 - val_mse: 0.1290\n",
      "Epoch 76/150\n",
      "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 180ms/step - cos: 0.7676 - loss: 5.4072 - mse: 0.1294 - val_cos: 0.7708 - val_loss: 5.3982 - val_mse: 0.1279\n",
      "Epoch 77/150\n",
      "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 179ms/step - cos: 0.7688 - loss: 5.3941 - mse: 0.1292 - val_cos: 0.7698 - val_loss: 5.4304 - val_mse: 0.1282\n",
      "Epoch 78/150\n",
      "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 179ms/step - cos: 0.7692 - loss: 5.4147 - mse: 0.1282 - val_cos: 0.7707 - val_loss: 5.3892 - val_mse: 0.1265\n",
      "Epoch 79/150\n",
      "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 177ms/step - cos: 0.7703 - loss: 5.3819 - mse: 0.1260 - val_cos: 0.7696 - val_loss: 5.3757 - val_mse: 0.1265\n",
      "Epoch 80/150\n",
      "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 181ms/step - cos: 0.7691 - loss: 5.3872 - mse: 0.1262 - val_cos: 0.7688 - val_loss: 5.3662 - val_mse: 0.1262\n",
      "Epoch 81/150\n",
      "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 179ms/step - cos: 0.7701 - loss: 5.3129 - mse: 0.1245 - val_cos: 0.7701 - val_loss: 5.3568 - val_mse: 0.1245\n",
      "Epoch 82/150\n",
      "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 182ms/step - cos: 0.7690 - loss: 5.3379 - mse: 0.1245 - val_cos: 0.7694 - val_loss: 5.3354 - val_mse: 0.1242\n",
      "Epoch 83/150\n",
      "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 180ms/step - cos: 0.7687 - loss: 5.3438 - mse: 0.1245 - val_cos: 0.7719 - val_loss: 5.3168 - val_mse: 0.1228\n",
      "Epoch 84/150\n",
      "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 182ms/step - cos: 0.7681 - loss: 5.3040 - mse: 0.1235 - val_cos: 0.7715 - val_loss: 5.3151 - val_mse: 0.1220\n",
      "Epoch 85/150\n",
      "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 183ms/step - cos: 0.7685 - loss: 5.3504 - mse: 0.1237 - val_cos: 0.7695 - val_loss: 5.3025 - val_mse: 0.1231\n",
      "Epoch 86/150\n",
      "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 174ms/step - cos: 0.7685 - loss: 5.3010 - mse: 0.1224 - val_cos: 0.7705 - val_loss: 5.3040 - val_mse: 0.1212\n",
      "Epoch 87/150\n",
      "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 183ms/step - cos: 0.7702 - loss: 5.2738 - mse: 0.1207 - val_cos: 0.7702 - val_loss: 5.2965 - val_mse: 0.1218\n",
      "Epoch 88/150\n",
      "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 180ms/step - cos: 0.7689 - loss: 5.2917 - mse: 0.1206 - val_cos: 0.7699 - val_loss: 5.2888 - val_mse: 0.1208\n",
      "Epoch 89/150\n",
      "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 180ms/step - cos: 0.7696 - loss: 5.3199 - mse: 0.1208 - val_cos: 0.7689 - val_loss: 5.2589 - val_mse: 0.1208\n",
      "Epoch 90/150\n",
      "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 179ms/step - cos: 0.7682 - loss: 5.2979 - mse: 0.1212 - val_cos: 0.7711 - val_loss: 5.2490 - val_mse: 0.1197\n",
      "Epoch 91/150\n",
      "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 174ms/step - cos: 0.7701 - loss: 5.2316 - mse: 0.1198 - val_cos: 0.7712 - val_loss: 5.2642 - val_mse: 0.1194\n",
      "Epoch 92/150\n",
      "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 182ms/step - cos: 0.7691 - loss: 5.2812 - mse: 0.1199 - val_cos: 0.7704 - val_loss: 5.2346 - val_mse: 0.1190\n",
      "Epoch 93/150\n",
      "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 175ms/step - cos: 0.7688 - loss: 5.2679 - mse: 0.1191 - val_cos: 0.7693 - val_loss: 5.2493 - val_mse: 0.1184\n",
      "Epoch 94/150\n",
      "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 179ms/step - cos: 0.7690 - loss: 5.2947 - mse: 0.1185 - val_cos: 0.7703 - val_loss: 5.2468 - val_mse: 0.1179\n",
      "Epoch 95/150\n",
      "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 181ms/step - cos: 0.7697 - loss: 5.2224 - mse: 0.1174 - val_cos: 0.7699 - val_loss: 5.2175 - val_mse: 0.1174\n",
      "Epoch 96/150\n",
      "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 180ms/step - cos: 0.7679 - loss: 5.2491 - mse: 0.1178 - val_cos: 0.7706 - val_loss: 5.2031 - val_mse: 0.1174\n",
      "Epoch 97/150\n",
      "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 176ms/step - cos: 0.7704 - loss: 5.2146 - mse: 0.1168 - val_cos: 0.7690 - val_loss: 5.1959 - val_mse: 0.1174\n",
      "Epoch 98/150\n",
      "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 178ms/step - cos: 0.7698 - loss: 5.1986 - mse: 0.1171 - val_cos: 0.7694 - val_loss: 5.1951 - val_mse: 0.1169\n",
      "Epoch 99/150\n",
      "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 175ms/step - cos: 0.7685 - loss: 5.1510 - mse: 0.1173 - val_cos: 0.7692 - val_loss: 5.2092 - val_mse: 0.1164\n",
      "Epoch 100/150\n",
      "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 174ms/step - cos: 0.7700 - loss: 5.1515 - mse: 0.1160 - val_cos: 0.7696 - val_loss: 5.2035 - val_mse: 0.1160\n",
      "Epoch 101/150\n",
      "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 181ms/step - cos: 0.7685 - loss: 5.2375 - mse: 0.1161 - val_cos: 0.7713 - val_loss: 5.1944 - val_mse: 0.1159\n",
      "Epoch 102/150\n",
      "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 175ms/step - cos: 0.7689 - loss: 5.1949 - mse: 0.1157 - val_cos: 0.7705 - val_loss: 5.1947 - val_mse: 0.1150\n",
      "Epoch 103/150\n",
      "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 181ms/step - cos: 0.7692 - loss: 5.1795 - mse: 0.1150 - val_cos: 0.7703 - val_loss: 5.1872 - val_mse: 0.1147\n",
      "Epoch 104/150\n",
      "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 179ms/step - cos: 0.7689 - loss: 5.1701 - mse: 0.1155 - val_cos: 0.7706 - val_loss: 5.1679 - val_mse: 0.1149\n",
      "Epoch 105/150\n",
      "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 173ms/step - cos: 0.7685 - loss: 5.1989 - mse: 0.1154 - val_cos: 0.7689 - val_loss: 5.1848 - val_mse: 0.1153\n",
      "Epoch 106/150\n",
      "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 182ms/step - cos: 0.7691 - loss: 5.1822 - mse: 0.1145 - val_cos: 0.7703 - val_loss: 5.1448 - val_mse: 0.1142\n",
      "Epoch 107/150\n",
      "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 175ms/step - cos: 0.7695 - loss: 5.1392 - mse: 0.1146 - val_cos: 0.7708 - val_loss: 5.1465 - val_mse: 0.1139\n",
      "Epoch 108/150\n",
      "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 177ms/step - cos: 0.7692 - loss: 5.2153 - mse: 0.1145 - val_cos: 0.7705 - val_loss: 5.1640 - val_mse: 0.1136\n",
      "Epoch 109/150\n",
      "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 176ms/step - cos: 0.7690 - loss: 5.1583 - mse: 0.1140 - val_cos: 0.7689 - val_loss: 5.1519 - val_mse: 0.1142\n",
      "Epoch 110/150\n",
      "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 178ms/step - cos: 0.7700 - loss: 5.1384 - mse: 0.1134 - val_cos: 0.7688 - val_loss: 5.1593 - val_mse: 0.1139\n",
      "Epoch 111/150\n",
      "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 180ms/step - cos: 0.7695 - loss: 5.1484 - mse: 0.1134 - val_cos: 0.7709 - val_loss: 5.1299 - val_mse: 0.1132\n",
      "Epoch 112/150\n",
      "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 181ms/step - cos: 0.7699 - loss: 5.1683 - mse: 0.1126 - val_cos: 0.7698 - val_loss: 5.1275 - val_mse: 0.1131\n",
      "Epoch 113/150\n",
      "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 171ms/step - cos: 0.7694 - loss: 5.1230 - mse: 0.1123 - val_cos: 0.7703 - val_loss: 5.1364 - val_mse: 0.1121\n",
      "Epoch 114/150\n",
      "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 178ms/step - cos: 0.7699 - loss: 5.1434 - mse: 0.1129 - val_cos: 0.7691 - val_loss: 5.1523 - val_mse: 0.1132\n",
      "Epoch 115/150\n",
      "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 178ms/step - cos: 0.7686 - loss: 5.1086 - mse: 0.1123 - val_cos: 0.7695 - val_loss: 5.1388 - val_mse: 0.1123\n",
      "Epoch 116/150\n",
      "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 177ms/step - cos: 0.7700 - loss: 5.1089 - mse: 0.1121 - val_cos: 0.7698 - val_loss: 5.1056 - val_mse: 0.1125\n",
      "Epoch 117/150\n",
      "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 176ms/step - cos: 0.7708 - loss: 5.0898 - mse: 0.1122 - val_cos: 0.7715 - val_loss: 5.1041 - val_mse: 0.1120\n",
      "Epoch 118/150\n",
      "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 174ms/step - cos: 0.7688 - loss: 5.1048 - mse: 0.1123 - val_cos: 0.7698 - val_loss: 5.1103 - val_mse: 0.1117\n",
      "Epoch 119/150\n",
      "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 177ms/step - cos: 0.7690 - loss: 5.1339 - mse: 0.1123 - val_cos: 0.7707 - val_loss: 5.0992 - val_mse: 0.1114\n",
      "Epoch 120/150\n",
      "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 174ms/step - cos: 0.7707 - loss: 5.0996 - mse: 0.1114 - val_cos: 0.7691 - val_loss: 5.1405 - val_mse: 0.1121\n",
      "Epoch 121/150\n",
      "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 176ms/step - cos: 0.7706 - loss: 5.0921 - mse: 0.1117 - val_cos: 0.7705 - val_loss: 5.1123 - val_mse: 0.1117\n",
      "Epoch 122/150\n",
      "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 175ms/step - cos: 0.7694 - loss: 5.1215 - mse: 0.1118 - val_cos: 0.7730 - val_loss: 5.1020 - val_mse: 0.1101\n",
      "Epoch 123/150\n",
      "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 173ms/step - cos: 0.7694 - loss: 5.1185 - mse: 0.1113 - val_cos: 0.7713 - val_loss: 5.1067 - val_mse: 0.1113\n",
      "Epoch 124/150\n",
      "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 175ms/step - cos: 0.7676 - loss: 5.1077 - mse: 0.1121 - val_cos: 0.7699 - val_loss: 5.1011 - val_mse: 0.1119\n",
      "Epoch 125/150\n",
      "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 178ms/step - cos: 0.7692 - loss: 5.1002 - mse: 0.1116 - val_cos: 0.7722 - val_loss: 5.0920 - val_mse: 0.1106\n",
      "Epoch 126/150\n",
      "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 179ms/step - cos: 0.7700 - loss: 5.0861 - mse: 0.1109 - val_cos: 0.7708 - val_loss: 5.0755 - val_mse: 0.1110\n",
      "Epoch 127/150\n",
      "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 176ms/step - cos: 0.7687 - loss: 5.1179 - mse: 0.1116 - val_cos: 0.7701 - val_loss: 5.0813 - val_mse: 0.1113\n",
      "Epoch 128/150\n",
      "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 176ms/step - cos: 0.7691 - loss: 5.0677 - mse: 0.1114 - val_cos: 0.7712 - val_loss: 5.0920 - val_mse: 0.1111\n",
      "Epoch 129/150\n",
      "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 182ms/step - cos: 0.7693 - loss: 5.0750 - mse: 0.1109 - val_cos: 0.7697 - val_loss: 5.1003 - val_mse: 0.1117\n",
      "Epoch 130/150\n",
      "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 180ms/step - cos: 0.7696 - loss: 5.1088 - mse: 0.1111 - val_cos: 0.7700 - val_loss: 5.1090 - val_mse: 0.1112\n",
      "Epoch 131/150\n",
      "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 180ms/step - cos: 0.7710 - loss: 5.0843 - mse: 0.1103 - val_cos: 0.7703 - val_loss: 5.0754 - val_mse: 0.1116\n",
      "Epoch 132/150\n",
      "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 178ms/step - cos: 0.7694 - loss: 5.0816 - mse: 0.1113 - val_cos: 0.7695 - val_loss: 5.0800 - val_mse: 0.1109\n",
      "Epoch 133/150\n",
      "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 183ms/step - cos: 0.7690 - loss: 5.0900 - mse: 0.1110 - val_cos: 0.7691 - val_loss: 5.1067 - val_mse: 0.1107\n",
      "Epoch 134/150\n",
      "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 179ms/step - cos: 0.7687 - loss: 5.1286 - mse: 0.1116 - val_cos: 0.7706 - val_loss: 5.0937 - val_mse: 0.1104\n",
      "Epoch 135/150\n",
      "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 179ms/step - cos: 0.7699 - loss: 5.0638 - mse: 0.1106 - val_cos: 0.7692 - val_loss: 5.1000 - val_mse: 0.1115\n",
      "Epoch 136/150\n",
      "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 178ms/step - cos: 0.7696 - loss: 5.0928 - mse: 0.1109 - val_cos: 0.7711 - val_loss: 5.1196 - val_mse: 0.1105\n",
      "Epoch 137/150\n",
      "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 178ms/step - cos: 0.7688 - loss: 5.0861 - mse: 0.1113 - val_cos: 0.7689 - val_loss: 5.0883 - val_mse: 0.1112\n",
      "Epoch 138/150\n",
      "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 182ms/step - cos: 0.7705 - loss: 5.0776 - mse: 0.1104 - val_cos: 0.7706 - val_loss: 5.0706 - val_mse: 0.1108\n",
      "Epoch 139/150\n",
      "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 180ms/step - cos: 0.7708 - loss: 5.0805 - mse: 0.1106 - val_cos: 0.7694 - val_loss: 5.0848 - val_mse: 0.1114\n",
      "Epoch 140/150\n",
      "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 183ms/step - cos: 0.7709 - loss: 5.0705 - mse: 0.1100 - val_cos: 0.7696 - val_loss: 5.1025 - val_mse: 0.1108\n",
      "Epoch 141/150\n",
      "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 184ms/step - cos: 0.7689 - loss: 5.0755 - mse: 0.1111 - val_cos: 0.7695 - val_loss: 5.0697 - val_mse: 0.1109\n",
      "Epoch 142/150\n",
      "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 176ms/step - cos: 0.7693 - loss: 5.0860 - mse: 0.1110 - val_cos: 0.7698 - val_loss: 5.0901 - val_mse: 0.1108\n",
      "Epoch 143/150\n",
      "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 182ms/step - cos: 0.7703 - loss: 5.0945 - mse: 0.1105 - val_cos: 0.7703 - val_loss: 5.0849 - val_mse: 0.1110\n",
      "Epoch 144/150\n",
      "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 181ms/step - cos: 0.7682 - loss: 5.0852 - mse: 0.1109 - val_cos: 0.7705 - val_loss: 5.0823 - val_mse: 0.1107\n",
      "Epoch 145/150\n",
      "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 183ms/step - cos: 0.7700 - loss: 5.0820 - mse: 0.1099 - val_cos: 0.7691 - val_loss: 5.0824 - val_mse: 0.1114\n",
      "Epoch 146/150\n",
      "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 184ms/step - cos: 0.7698 - loss: 5.1090 - mse: 0.1105 - val_cos: 0.7697 - val_loss: 5.0849 - val_mse: 0.1113\n",
      "Epoch 147/150\n",
      "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 177ms/step - cos: 0.7699 - loss: 5.0637 - mse: 0.1106 - val_cos: 0.7702 - val_loss: 5.0996 - val_mse: 0.1107\n",
      "Epoch 148/150\n",
      "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 179ms/step - cos: 0.7708 - loss: 5.0515 - mse: 0.1101 - val_cos: 0.7695 - val_loss: 5.0811 - val_mse: 0.1111\n",
      "Epoch 149/150\n",
      "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 192ms/step - cos: 0.7692 - loss: 5.0959 - mse: 0.1111 - val_cos: 0.7705 - val_loss: 5.1056 - val_mse: 0.1106\n",
      "Epoch 150/150\n",
      "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 178ms/step - cos: 0.7685 - loss: 5.1008 - mse: 0.1110 - val_cos: 0.7713 - val_loss: 5.0885 - val_mse: 0.1105\n"
     ]
    }
   ],
   "source": [
    "history = model.fit(\n",
    "    train_ds,\n",
    "    steps_per_epoch=steps_per_epoch,\n",
    "    verbose=verbose,\n",
    "    epochs=epochs,\n",
    "    validation_data=val_ds,\n",
    "    callbacks=model_callbacks,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visualize training history\n",
    "\n",
    "Let's visualize the training history to understand the model's performance during training. This will help to ensure the model is learning and not under or overfitting."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 900x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, _ = nse.plotting.plot_history_metrics(\n",
    "    history.history,\n",
    "    metrics=[\"loss\", \"cos\"],\n",
    "    title=\"Training History\",\n",
    "    colors=[plot_theme.primary_color, plot_theme.secondary_color],\n",
    "    stack=True,\n",
    "    figsize=(9, 5),\n",
    ")\n",
    "fig.tight_layout()\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model evaluation\n",
    "\n",
    "Now that we have trained the model, we will evaluate the model on the test dataset. The model's built-in `evaluate` method will be used to calculate the loss and metrics on the dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Convert validation dataset to numpy arrays\n",
    "test_x1, test_x2 = [], []\n",
    "for inputs in val_ds.as_numpy_iterator():\n",
    "    test_x1.append(inputs[nse.trainers.SimCLRTrainer.AUG_SAMPLES_0])\n",
    "    test_x2.append(inputs[nse.trainers.SimCLRTrainer.AUG_SAMPLES_1])\n",
    "test_x1 = np.concatenate(test_x1)\n",
    "test_x2 = np.concatenate(test_x2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m288/288\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 923us/step\n",
      "\u001b[1m288/288\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step\n"
     ]
    }
   ],
   "source": [
    "test_y1 = encoder.predict(test_x1)\n",
    "test_y2 = encoder.predict(test_x2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #000080; text-decoration-color: #000080\">INFO    </span> <span style=\"font-weight: bold\">[</span>VAL SET<span style=\"font-weight: bold\">]</span> <span style=\"color: #808000; text-decoration-color: #808000\">MSE</span>=<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.0132</span>, <span style=\"color: #808000; text-decoration-color: #808000\">COS</span>=<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.9683</span>                                                           <a href=\"file:///tmp/ipykernel_712291/4122487501.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">4122487501.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///tmp/ipykernel_712291/4122487501.py#2\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">2</span></a>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[34mINFO    \u001b[0m \u001b[1m[\u001b[0mVAL SET\u001b[1m]\u001b[0m \u001b[33mMSE\u001b[0m=\u001b[1;36m0\u001b[0m\u001b[1;36m.0132\u001b[0m, \u001b[33mCOS\u001b[0m=\u001b[1;36m0\u001b[0m\u001b[1;36m.9683\u001b[0m                                                           \u001b]8;id=945728;file:///tmp/ipykernel_712291/4122487501.py\u001b\\\u001b[2m4122487501.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=966210;file:///tmp/ipykernel_712291/4122487501.py#2\u001b\\\u001b[2m2\u001b[0m\u001b]8;;\u001b\\\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "rst = nse.metrics.compute_metrics(metrics, test_y1, test_y2)\n",
    "logger.info(\"[VAL SET] \" + \", \".join([f\"{k.upper()}={v:.4f}\" for k, v in rst.items()]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Export model to TF Lite / TFLM\n",
    "\n",
    "Once we have trained and evaluated the model, we need to export the model into a format that can be used for inference on the edge. Currently, we export the model to TensorFlow Lite flatbuffer format. This will also generate a C header file that can be used with TensorFlow Lite for Microcontrollers (TFLM).\n",
    "\n",
    "For this model, we will export as a 32-bit floating point model.\n",
    " \n",
    "__NOTE:__ We utilize `CONCRETE` mode to lower the model to concrete functions before converting. This is because TF (MLIR) fails to properly lower the dilated convolutional layers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "W0000 00:00:1723835186.987318  712291 tf_tfl_flatbuffer_helpers.cc:392] Ignored output_format.\n",
      "W0000 00:00:1723835186.987329  712291 tf_tfl_flatbuffer_helpers.cc:395] Ignored drop_control_dependency.\n"
     ]
    }
   ],
   "source": [
    "converter = nse.converters.tflite.TfLiteKerasConverter(model=encoder)\n",
    "\n",
    "# Redirect stdout and stderr to devnull since TFLite converter is very verbose\n",
    "with open(os.devnull, 'w') as devnull:\n",
    "    with contextlib.redirect_stdout(devnull), contextlib.redirect_stderr(devnull):\n",
    "        tflite_content = converter.convert(\n",
    "            test_x=test_x1,\n",
    "            quantization=\"FP32\",\n",
    "            io_type=\"float32\",\n",
    "            mode=\"KERAS\",\n",
    "            strict=False,\n",
    "            verbose=verbose\n",
    "        )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Save TFLite model as both a file and C header"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "converter.export(\n",
    "    tflite_path=job_dir / \"model.tflite\"\n",
    ")\n",
    "\n",
    "converter.export_header(\n",
    "    header_path=job_dir / \"model.h\",\n",
    "    name=\"model\",\n",
    ")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Evaluate TFLite model against TensorFlow model\n",
    "\n",
    "We will instantiate a tflite interpreter and evaluate the model on the test dataset. This will help us ensure that the model has been exported correctly and is ready for deployment."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO: Created TensorFlow Lite XNNPACK delegate for CPU.\n"
     ]
    }
   ],
   "source": [
    "tflite = nse.interpreters.tflite.TfLiteKerasInterpreter(tflite_content)\n",
    "tflite.compile()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saved artifact at '/tmp/tmpserse9cu'. The following endpoints are available:\n",
      "\n",
      "* Endpoint 'serve'\n",
      "  args_0 (POSITIONAL_ONLY): TensorSpec(shape=(None, 800, 1), dtype=tf.float32, name='input')\n",
      "Output Type:\n",
      "  TensorSpec(shape=(None, 128), dtype=tf.float32, name=None)\n",
      "Captures:\n",
      "  132164125518800: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
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     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "W0000 00:00:1723835188.716817  712291 tf_tfl_flatbuffer_helpers.cc:392] Ignored output_format.\n",
      "W0000 00:00:1723835188.716827  712291 tf_tfl_flatbuffer_helpers.cc:395] Ignored drop_control_dependency.\n"
     ]
    }
   ],
   "source": [
    "converter = nse.converters.tflite.TfLiteKerasConverter(model=encoder)\n",
    "\n",
    "tflite_content = converter.convert(\n",
    "    test_x=test_x1,\n",
    "    quantization=\"FP32\",\n",
    "    io_type=\"float32\",\n",
    "    mode=\"KERAS\",\n",
    "    strict=False,\n",
    "    verbose=verbose\n",
    ")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "tflite = nse.interpreters.tflite.TfLiteKerasInterpreter(tflite_content)\n",
    "tflite.compile()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m  1/288\u001b[0m \u001b[37m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m2s\u001b[0m 9ms/step"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m288/288\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step\n",
      "\u001b[1m288/288\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step\n"
     ]
    }
   ],
   "source": [
    "y1_pred_tf = encoder.predict(test_x1)\n",
    "y2_pred_tf = encoder.predict(test_x2)\n",
    "\n",
    "y1_pred_tfl = tflite.predict(x=test_x1)\n",
    "y2_pred_tfl = tflite.predict(x=test_x2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #000080; text-decoration-color: #000080\">INFO    </span> <span style=\"font-weight: bold\">[</span>TF METRICS<span style=\"font-weight: bold\">]</span> <span style=\"color: #808000; text-decoration-color: #808000\">MSE</span>=<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.0132</span> <span style=\"color: #808000; text-decoration-color: #808000\">COS</span>=<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.9683</span>                                                         <a href=\"file:///tmp/ipykernel_712291/2850812944.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">2850812944.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///tmp/ipykernel_712291/2850812944.py#3\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">3</span></a>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[34mINFO    \u001b[0m \u001b[1m[\u001b[0mTF METRICS\u001b[1m]\u001b[0m \u001b[33mMSE\u001b[0m=\u001b[1;36m0\u001b[0m\u001b[1;36m.0132\u001b[0m \u001b[33mCOS\u001b[0m=\u001b[1;36m0\u001b[0m\u001b[1;36m.9683\u001b[0m                                                         \u001b]8;id=395402;file:///tmp/ipykernel_712291/2850812944.py\u001b\\\u001b[2m2850812944.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=569945;file:///tmp/ipykernel_712291/2850812944.py#3\u001b\\\u001b[2m3\u001b[0m\u001b]8;;\u001b\\\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #000080; text-decoration-color: #000080\">INFO    </span> <span style=\"font-weight: bold\">[</span>TFL METRICS<span style=\"font-weight: bold\">]</span> <span style=\"color: #808000; text-decoration-color: #808000\">MSE</span>=<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.0132</span> <span style=\"color: #808000; text-decoration-color: #808000\">COS</span>=<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.9683</span>                                                        <a href=\"file:///tmp/ipykernel_712291/2850812944.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">2850812944.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///tmp/ipykernel_712291/2850812944.py#4\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">4</span></a>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[34mINFO    \u001b[0m \u001b[1m[\u001b[0mTFL METRICS\u001b[1m]\u001b[0m \u001b[33mMSE\u001b[0m=\u001b[1;36m0\u001b[0m\u001b[1;36m.0132\u001b[0m \u001b[33mCOS\u001b[0m=\u001b[1;36m0\u001b[0m\u001b[1;36m.9683\u001b[0m                                                        \u001b]8;id=984174;file:///tmp/ipykernel_712291/2850812944.py\u001b\\\u001b[2m2850812944.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=540128;file:///tmp/ipykernel_712291/2850812944.py#4\u001b\\\u001b[2m4\u001b[0m\u001b]8;;\u001b\\\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tf_rst = nse.metrics.compute_metrics(metrics, y1_pred_tf, y2_pred_tf)\n",
    "tfl_rst = nse.metrics.compute_metrics(metrics, y1_pred_tfl, y2_pred_tfl)\n",
    "logger.info(\"[TF METRICS] \" + \" \".join([f\"{k.upper()}={v:.4f}\" for k, v in tf_rst.items()]))\n",
    "logger.info(\"[TFL METRICS] \" + \" \".join([f\"{k.upper()}={v:.4f}\" for k, v in tfl_rst.items()]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## ECG Foundation Demo\n",
    "\n",
    "Finally, we will showcase the foundation model by running across lots of patients and plotting via t-SNE to view the embeddings. This will help us understand how the model is clustering the data and if it is learning useful features."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Compute t-SNE\n",
    "logger.debug(\"Computing t-SNE\")\n",
    "tsne = TSNE(n_components=2, random_state=0, n_iter=1000, perplexity=75)\n",
    "x_tsne = tsne.fit_transform(test_y1)\n",
    "\n",
    "# Plot t-SNE in matplotlib\n",
    "fig, ax = plt.subplots(1, 1, figsize=(6, 6))\n",
    "ax.scatter(x_tsne[:, 0], x_tsne[:, 1], c=x_tsne[:, 0] - x_tsne[:, 1], cmap=\"viridis\")\n",
    "fig.suptitle(\"HK Foundation: t-SNE\")\n",
    "ax.set_xlabel(\"Component 1\")\n",
    "ax.set_ylabel(\"Component 2\")\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": ".venv",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
